Analysis date: 2023-10-18

Depends on

CRC_Xenografts_Batch2_DataProcessing Script

load("../Data/Cache/Xenografts_Batch2_DataProcessing.RData")

TODO

Setup

Load libraries and functions

Analysis

DEP

Tyrosine

E vs ctrl

data_diff_E_vs_ctrl_pY <- test_diff(pY_se_Set2, type="manual", test = "E_vs_ctrl")
## Tested contrasts: E_vs_ctrl
dep_E_vs_ctrl_pY <- add_rejections_SH(data_diff_E_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_E_vs_ctrl_pY, contrast = "E_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY") 
Return_DEP_Hits_Plots(data = pY_Set2_form, dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## Loading required namespace: reactome.db
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## character(0)
PTM-SEA
GSEA_E_vs_ctrl_PTM <- Run_GSEA(DEP_result = dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PATH-NP_EGFR1_PATHWAY"
GSEA_E_vs_ctrl_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))
## # A tibble: 1 × 8
##   pathway                     pval    padj log2err    ES   NES  size leadingEdge
##   <chr>                      <dbl>   <dbl>   <dbl> <dbl> <dbl> <int> <list>     
## 1 PATH-NP_EGFR1_PATHWAY 0.00000702 0.00247   0.611 0.535  1.58   116 <chr [48]>
Run_GSEA(DEP_result = dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PATH-NP_EGFR1_PATHWAY"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 254 × 4
##    HGNC_Symbol Annotated_Sequence             MOD_RSD    FC
##    <chr>       <chr>                          <chr>   <dbl>
##  1 PXN         VGEEEHVySFPNK                  Y118-p   2.34
##  2 PXN         vGEEEHVySFPNk                  Y118-p   2.34
##  3 NEDD9       DGVyDVPLHNPPDAK                Y345-p   2.27
##  4 CTTN        LPSSPVyEDAASFK                 Y421-p   1.60
##  5 CTTN        lPSSPVyEDAASFk                 Y421-p   1.60
##  6 CTTN        TQTPPVSPAPQPTEERLPSSPVyEDAASFK Y421-p   1.60
##  7 GAB1        KDASSQDCyDIPR                  Y406-p   1.59
##  8 NCK1        LyDLNMPAYVK                    Y105-p   1.56
##  9 NCK1        lyDLNMPAYVk                    Y105-p   1.56
## 10 TNS3        KLSLGQyDNDAGGQLPFSK            Y780-p   1.53
## # ℹ 244 more rows
Run_GSEA(DEP_result = dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PATH-NP_EGFR1_PATHWAY"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 78 × 4
##    HGNC_Symbol Annotated_Sequence   MOD_RSD    FC
##    <chr>       <chr>                <chr>   <dbl>
##  1 SHC1        ELFDDPSyVNVQNLDK     Y427-p   1.45
##  2 SHC1        eLFDDPSyVNVQNLDk     Y427-p   1.45
##  3 PRKCD       KTGVAGEDMQDNSGTyGK   Y334-p   1.38
##  4 PRKCD       TGVAGEDMQDNSGTyGK    Y334-p   1.38
##  5 PRKCD       tGVAGEDMQDNSGTyGk    Y334-p   1.38
##  6 PXN         FIHQQPQSSSPVyGSSAK   Y88-p    1.36
##  7 CTTN        NASTFEDVTQVSSAyQK    Y334-p   1.15
##  8 CTTN        MDKNASTFEDVTQVSSAyQK Y334-p   1.15
##  9 CTTN        nASTFEDVTQVSSAyQk    Y334-p   1.15
## 10 DAPP1       KVEEPSIyESVR         Y139-p   1.11
## # ℹ 68 more rows
Run_GSEA(DEP_result = dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PATH-NP_EGFR1_PATHWAY"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 12 × 4
##    HGNC_Symbol Annotated_Sequence   MOD_RSD      FC
##    <chr>       <chr>                <chr>     <dbl>
##  1 EPHA2       TyVDPHTYEDPNQAVLK    Y588-p   0.622 
##  2 EPHA2       TYVDPHTyEDPNQAVLK    Y594-p   0.314 
##  3 EPHA2       tYVDPHTyEDPNQAVLk    Y594-p   0.314 
##  4 EPHA2       VLEDDPEATyTTSGGK     Y772-p   0.310 
##  5 EPHA2       VLEDDPEATyTTSGGKIPIR Y772-p   0.310 
##  6 EPHA2       vLEDDPEATyTTSGGk     Y772-p   0.310 
##  7 EPHA2       vLEDDPEATyTTSGGkIPIR Y772-p   0.310 
##  8 EPHA2       QSPEDVyFSK           Y575-p   0.174 
##  9 EPHA2       qSPEDVyFSk           Y575-p   0.174 
## 10 CLDN4       SAAASNyV             Y208-p   0.0688
## 11 CLDN4       sAAASNyV             Y208-p   0.0688
## 12 EPHA2       VIGAGEFGEVyKGMLK     Y628-p  -0.0207

EC vs ctrl

data_diff_EC_vs_ctrl_pY <- test_diff(pY_se_Set2, type="manual", test = "EC_vs_ctrl")
## Tested contrasts: EC_vs_ctrl
dep_EC_vs_ctrl_pY <- add_rejections_SH(data_diff_EC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pY, contrast = "EC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY") 
Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## character(0)
## Warning in min(screen_pval05_neg[, logFcColStr]): no non-missing arguments to
## min; returning Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

## Note: Row-scaling applied for this heatmap

Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", 
                               pw = "Epigenetic regulation of gene expression")
PTM-SEA
GSEA_EC_vs_ctrl_PTM <- Run_GSEA(DEP_result = dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "KINASE-PSP_EphA2/EPHA2"
GSEA_EC_vs_ctrl_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))
## # A tibble: 1 × 8
##   pathway                    pval    padj log2err     ES   NES  size leadingEdge
##   <chr>                     <dbl>   <dbl>   <dbl>  <dbl> <dbl> <int> <list>     
## 1 KINASE-PSP_EphA2/EPHA2  1.84e-5 0.00649   0.576 -0.932 -2.30     6 <chr [6]>
Run_GSEA(DEP_result = dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "KINASE-PSP_EphA2/EPHA2"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 254 × 4
##    HGNC_Symbol Annotated_Sequence MOD_RSD    FC
##    <chr>       <chr>              <chr>   <dbl>
##  1 NCK1        LyDLNMPAYVK        Y105-p   1.85
##  2 NCK1        lyDLNMPAYVk        Y105-p   1.85
##  3 MAPK14      HTDDEMTGyVATR      Y182-p   1.53
##  4 MAPK14      HTDDEMtGyVATR      Y182-p   1.53
##  5 MAPK14      hTDDEMTGyVATR      Y182-p   1.53
##  6 MAPK14      hTDDEMtGyVATR      Y182-p   1.53
##  7 MAPK14      hTDDEmTGyVATR      Y182-p   1.53
##  8 MAPK14      hTDDEmtGyVATR      Y182-p   1.53
##  9 GRB7        DASRPHVVKVySEDGACR Y107-p   1.50
## 10 PXN         VGEEEHVySFPNK      Y118-p   1.48
## # ℹ 244 more rows
Run_GSEA(DEP_result = dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "KINASE-PSP_EphA2/EPHA2"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 78 × 4
##    HGNC_Symbol Annotated_Sequence                MOD_RSD    FC
##    <chr>       <chr>                             <chr>   <dbl>
##  1 ARHGAP35    NEEENIySVPHDSTQGK                 Y1105-p  1.80
##  2 ARHGAP35    nEEENIySVPHDSTQGk                 Y1105-p  1.80
##  3 PRKCD       KTGVAGEDMQDNSGTyGK                Y334-p   1.30
##  4 PRKCD       TGVAGEDMQDNSGTyGK                 Y334-p   1.30
##  5 PRKCD       tGVAGEDMQDNSGTyGk                 Y334-p   1.30
##  6 SRC         EPEERPTFEYLQAFLEDYFTSTEPQyQPGENL  Y530-p   1.29
##  7 SRC         kEPEERPTFEYLQAFLEDYFTSTEPQyQPGENL Y530-p   1.29
##  8 PRKCD       RSDSASSEPVGIyQGFEK                Y313-p   1.19
##  9 PRKCD       RSDsASSEPVGIyQGFEK                Y313-p   1.19
## 10 PRKCD       RSDSASSEPVGIyQGFEKK               Y313-p   1.19
## # ℹ 68 more rows
Run_GSEA(DEP_result = dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "KINASE-PSP_EphA2/EPHA2"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 12 × 4
##    HGNC_Symbol Annotated_Sequence   MOD_RSD     FC
##    <chr>       <chr>                <chr>    <dbl>
##  1 CLDN4       SAAASNyV             Y208-p  -0.482
##  2 CLDN4       sAAASNyV             Y208-p  -0.482
##  3 EPHA2       TyVDPHTYEDPNQAVLK    Y588-p  -0.501
##  4 EPHA2       VIGAGEFGEVyKGMLK     Y628-p  -0.807
##  5 EPHA2       TYVDPHTyEDPNQAVLK    Y594-p  -0.877
##  6 EPHA2       tYVDPHTyEDPNQAVLk    Y594-p  -0.877
##  7 EPHA2       VLEDDPEATyTTSGGK     Y772-p  -0.968
##  8 EPHA2       VLEDDPEATyTTSGGKIPIR Y772-p  -0.968
##  9 EPHA2       vLEDDPEATyTTSGGk     Y772-p  -0.968
## 10 EPHA2       vLEDDPEATyTTSGGkIPIR Y772-p  -0.968
## 11 EPHA2       QSPEDVyFSK           Y575-p  -1.22 
## 12 EPHA2       qSPEDVyFSk           Y575-p  -1.22

EBC vs ctrl

data_diff_EBC_vs_ctrl_pY <- test_diff(pY_se_Set2, type="manual", test = "EBC_vs_ctrl")
## Tested contrasts: EBC_vs_ctrl
dep_EBC_vs_ctrl_pY <- add_rejections_SH(data_diff_EBC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pY, contrast = "EBC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY")
Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## character(0)

## Note: Row-scaling applied for this heatmap

PTM-SEA
GSEA_EBC_vs_ctrl_PTM <- Run_GSEA(DEP_result = dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "KINASE-PSP_EphA2/EPHA2"
GSEA_EBC_vs_ctrl_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))
## # A tibble: 1 × 8
##   pathway                     pval   padj log2err     ES   NES  size leadingEdge
##   <chr>                      <dbl>  <dbl>   <dbl>  <dbl> <dbl> <int> <list>     
## 1 KINASE-PSP_EphA2/EPHA2 0.0000378 0.0133   0.557 -0.931 -2.13     6 <chr [5]>
Run_GSEA(DEP_result = dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "KINASE-PSP_EphA2/EPHA2"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 254 × 4
##    HGNC_Symbol Annotated_Sequence MOD_RSD    FC
##    <chr>       <chr>              <chr>   <dbl>
##  1 GRB7        DASRPHVVKVySEDGACR Y107-p   1.55
##  2 DLG3        DNEVDGQDyHFVVSR    Y673-p   1.54
##  3 DLG3        RDNEVDGQDyHFVVSR   Y673-p   1.54
##  4 DLG3        dNEVDGQDyHFVVSR    Y673-p   1.54
##  5 DLG3        rDNEVDGQDyHFVVSR   Y673-p   1.54
##  6 PIK3R2      EYDQLyEEYTR        Y464-p   1.44
##  7 PIK3R2      SREYDQLyEEYTR      Y464-p   1.44
##  8 PIK3R2      sREYDQLyEEYTR      Y464-p   1.44
##  9 NCK1        LyDLNMPAYVK        Y105-p   1.41
## 10 NCK1        lyDLNMPAYVk        Y105-p   1.41
## # ℹ 244 more rows
Run_GSEA(DEP_result = dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "KINASE-PSP_EphA2/EPHA2"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 78 × 4
##    HGNC_Symbol Annotated_Sequence                MOD_RSD    FC
##    <chr>       <chr>                             <chr>   <dbl>
##  1 ARHGAP35    NEEENIySVPHDSTQGK                 Y1105-p 1.97 
##  2 ARHGAP35    nEEENIySVPHDSTQGk                 Y1105-p 1.97 
##  3 SRC         EPEERPTFEYLQAFLEDYFTSTEPQyQPGENL  Y530-p  1.36 
##  4 SRC         kEPEERPTFEYLQAFLEDYFTSTEPQyQPGENL Y530-p  1.36 
##  5 PTPRA       VVQEYIDAFSDyANFK                  Y798-p  0.777
##  6 PTPRA       vVQEYIDAFSDyANFk                  Y798-p  0.777
##  7 CDH1        yLPRPANPDEIGNFIDENLK              Y797-p  0.539
##  8 CDH1        yLPRPANPDEIGNFIDENLk              Y797-p  0.539
##  9 PRKCD       RSDSASSEPVGIyQGFEK                Y313-p  0.525
## 10 PRKCD       RSDsASSEPVGIyQGFEK                Y313-p  0.525
## # ℹ 68 more rows
Run_GSEA(DEP_result = dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "KINASE-PSP_EphA2/EPHA2"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 12 × 4
##    HGNC_Symbol Annotated_Sequence   MOD_RSD     FC
##    <chr>       <chr>                <chr>    <dbl>
##  1 CLDN4       SAAASNyV             Y208-p  -0.394
##  2 CLDN4       sAAASNyV             Y208-p  -0.394
##  3 EPHA2       VIGAGEFGEVyKGMLK     Y628-p  -0.953
##  4 EPHA2       TyVDPHTYEDPNQAVLK    Y588-p  -1.05 
##  5 EPHA2       VLEDDPEATyTTSGGK     Y772-p  -1.41 
##  6 EPHA2       VLEDDPEATyTTSGGKIPIR Y772-p  -1.41 
##  7 EPHA2       vLEDDPEATyTTSGGk     Y772-p  -1.41 
##  8 EPHA2       vLEDDPEATyTTSGGkIPIR Y772-p  -1.41 
##  9 EPHA2       TYVDPHTyEDPNQAVLK    Y594-p  -1.47 
## 10 EPHA2       tYVDPHTyEDPNQAVLk    Y594-p  -1.47 
## 11 EPHA2       QSPEDVyFSK           Y575-p  -1.80 
## 12 EPHA2       qSPEDVyFSk           Y575-p  -1.80

EC vs E

data_diff_EC_vs_E_pY <- test_diff(pY_se_Set2, type = "manual", 
                              test = c("EC_vs_E"))
## Tested contrasts: EC_vs_E
dep_EC_vs_E_pY <- add_rejections_SH(data_diff_EC_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pY, contrast = "EC_vs_E",  add_names = TRUE, additional_title = "pY", proteins_of_interest = "EGFR")
Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EC_vs_E_pY, comparison = "EC_vs_E_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## [1] "Metabolism of RNA"               "Interleukin-20 family signaling"

## Note: Row-scaling applied for this heatmap

#data_results <- get_df_long(dep)
PTM-SEA
GSEA_EC_vs_E_PTM <- Run_GSEA(DEP_result = dep_EC_vs_E_pY, comparison = "EC_vs_E_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "KINASE-PSP_EphA2/EPHA2"
GSEA_EC_vs_E_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))
## # A tibble: 1 × 8
##   pathway                    pval    padj log2err     ES   NES  size leadingEdge
##   <chr>                     <dbl>   <dbl>   <dbl>  <dbl> <dbl> <int> <list>     
## 1 KINASE-PSP_EphA2/EPHA2  1.09e-5 0.00384   0.593 -0.937 -1.89     6 <chr [6]>
Run_GSEA(DEP_result = dep_EC_vs_E_pY, comparison = "EC_vs_E_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "KINASE-PSP_EphA2/EPHA2"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 254 × 4
##    HGNC_Symbol Annotated_Sequence                MOD_RSD    FC
##    <chr>       <chr>                             <chr>   <dbl>
##  1 GRB7        DASRPHVVKVySEDGACR                Y107-p  0.991
##  2 SRC         EPEERPTFEYLQAFLEDYFTSTEPQyQPGENL  Y530-p  0.934
##  3 SRC         kEPEERPTFEYLQAFLEDYFTSTEPQyQPGENL Y530-p  0.934
##  4 ITGB4       VCAYGAQGEGPySSLVSCR               Y1207-p 0.483
##  5 ITGB4       vcAYGAQGEGPySSLVScR               Y1207-p 0.483
##  6 PRKCD       RSDSASSEPVGIyQGFEK                Y313-p  0.482
##  7 PRKCD       RSDsASSEPVGIyQGFEK                Y313-p  0.482
##  8 PRKCD       RSDSASSEPVGIyQGFEKK               Y313-p  0.482
##  9 PRKCD       SDSASSEPVGIyQGFEK                 Y313-p  0.482
## 10 PRKCD       SDsASSEPVGIyQGFEK                 Y313-p  0.482
## # ℹ 244 more rows
Run_GSEA(DEP_result = dep_EC_vs_E_pY, comparison = "EC_vs_E_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "KINASE-PSP_EphA2/EPHA2"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 78 × 4
##    HGNC_Symbol Annotated_Sequence                MOD_RSD    FC
##    <chr>       <chr>                             <chr>   <dbl>
##  1 ARHGAP35    NEEENIySVPHDSTQGK                 Y1105-p 1.31 
##  2 ARHGAP35    nEEENIySVPHDSTQGk                 Y1105-p 1.31 
##  3 SRC         EPEERPTFEYLQAFLEDYFTSTEPQyQPGENL  Y530-p  0.934
##  4 SRC         kEPEERPTFEYLQAFLEDYFTSTEPQyQPGENL Y530-p  0.934
##  5 PRKCD       RSDSASSEPVGIyQGFEK                Y313-p  0.482
##  6 PRKCD       RSDsASSEPVGIyQGFEK                Y313-p  0.482
##  7 PRKCD       RSDSASSEPVGIyQGFEKK               Y313-p  0.482
##  8 PRKCD       SDSASSEPVGIyQGFEK                 Y313-p  0.482
##  9 PRKCD       SDsASSEPVGIyQGFEK                 Y313-p  0.482
## 10 PRKCD       SDSASSEPVGIyQGFEKK                Y313-p  0.482
## # ℹ 68 more rows
Run_GSEA(DEP_result = dep_EC_vs_E_pY, comparison = "EC_vs_E_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "KINASE-PSP_EphA2/EPHA2"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 12 × 4
##    HGNC_Symbol Annotated_Sequence   MOD_RSD     FC
##    <chr>       <chr>                <chr>    <dbl>
##  1 CLDN4       SAAASNyV             Y208-p  -0.551
##  2 CLDN4       sAAASNyV             Y208-p  -0.551
##  3 EPHA2       VIGAGEFGEVyKGMLK     Y628-p  -0.786
##  4 EPHA2       TyVDPHTYEDPNQAVLK    Y588-p  -1.12 
##  5 EPHA2       TYVDPHTyEDPNQAVLK    Y594-p  -1.19 
##  6 EPHA2       tYVDPHTyEDPNQAVLk    Y594-p  -1.19 
##  7 EPHA2       VLEDDPEATyTTSGGK     Y772-p  -1.28 
##  8 EPHA2       VLEDDPEATyTTSGGKIPIR Y772-p  -1.28 
##  9 EPHA2       vLEDDPEATyTTSGGk     Y772-p  -1.28 
## 10 EPHA2       vLEDDPEATyTTSGGkIPIR Y772-p  -1.28 
## 11 EPHA2       QSPEDVyFSK           Y575-p  -1.39 
## 12 EPHA2       qSPEDVyFSk           Y575-p  -1.39

EBC vs EC

data_diff_EBC_vs_EC_pY <- test_diff(pY_se_Set2, type = "manual", 
                              test = c("EBC_vs_EC"))
## Tested contrasts: EBC_vs_EC
dep_EBC_vs_EC_pY <- add_rejections_SH(data_diff_EBC_vs_EC_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pY, contrast = "EBC_vs_EC",  add_names = TRUE, additional_title = "pY")
Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## [1] "Metabolism of nucleotides"   "Selenoamino acid metabolism"
## [3] "Metabolism"                  "Signaling by VEGF"
#data_results <- get_df_long(dep)
PTM-SEA
GSEA_EBC_vs_EC_PTM <- Run_GSEA(DEP_result = dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PATH-NP_EGFR1_PATHWAY"
GSEA_EBC_vs_EC_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))
## # A tibble: 1 × 8
##   pathway                    pval   padj log2err     ES   NES  size leadingEdge
##   <chr>                     <dbl>  <dbl>   <dbl>  <dbl> <dbl> <int> <list>     
## 1 PATH-NP_EGFR1_PATHWAY 0.0000775 0.0273   0.538 -0.501 -1.64   116 <chr [39]>
Run_GSEA(DEP_result = dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PATH-NP_EGFR1_PATHWAY"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 254 × 4
##    HGNC_Symbol Annotated_Sequence MOD_RSD    FC
##    <chr>       <chr>              <chr>   <dbl>
##  1 CDK5        IGEGTyGTVFK        Y15-p   0.759
##  2 ATP1A1      GIVVyTGDR          Y260-p  0.627
##  3 PIK3R1      DQyLMWLTQK         Y580-p  0.530
##  4 PIK3R1      TRDQyLMWLTQK       Y580-p  0.530
##  5 PIK3R1      dQyLMWLTQk         Y580-p  0.530
##  6 PIK3R1      dQyLmWLTQk         Y580-p  0.530
##  7 DLG3        DNEVDGQDyHFVVSR    Y673-p  0.452
##  8 DLG3        RDNEVDGQDyHFVVSR   Y673-p  0.452
##  9 DLG3        dNEVDGQDyHFVVSR    Y673-p  0.452
## 10 DLG3        rDNEVDGQDyHFVVSR   Y673-p  0.452
## # ℹ 244 more rows
Run_GSEA(DEP_result = dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PATH-NP_EGFR1_PATHWAY"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 78 × 4
##    HGNC_Symbol Annotated_Sequence               MOD_RSD     FC
##    <chr>       <chr>                            <chr>    <dbl>
##  1 ATP1A1      GIVVyTGDR                        Y260-p  0.627 
##  2 DDR2        NLySGDYYR                        Y736-p  0.219 
##  3 DDR2        NLySGDyYR                        Y736-p  0.219 
##  4 STAT3       YCRPESQEHPEADPGSAAPyLK           Y705-p  0.192 
##  5 ARHGAP35    NEEENIySVPHDSTQGK                Y1105-p 0.168 
##  6 ARHGAP35    nEEENIySVPHDSTQGk                Y1105-p 0.168 
##  7 SDC4        APTNEFyA                         Y197-p  0.142 
##  8 SDC4        KAPTNEFyA                        Y197-p  0.142 
##  9 PTK2        SNDKVyENVTGLVK                   Y925-p  0.0905
## 10 SRC         EPEERPTFEYLQAFLEDYFTSTEPQyQPGENL Y530-p  0.0657
## # ℹ 68 more rows
Run_GSEA(DEP_result = dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PATH-NP_EGFR1_PATHWAY"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 12 × 4
##    HGNC_Symbol Annotated_Sequence   MOD_RSD      FC
##    <chr>       <chr>                <chr>     <dbl>
##  1 CLDN4       SAAASNyV             Y208-p   0.0884
##  2 CLDN4       sAAASNyV             Y208-p   0.0884
##  3 EPHA2       VIGAGEFGEVyKGMLK     Y628-p  -0.146 
##  4 EPHA2       VLEDDPEATyTTSGGK     Y772-p  -0.441 
##  5 EPHA2       VLEDDPEATyTTSGGKIPIR Y772-p  -0.441 
##  6 EPHA2       vLEDDPEATyTTSGGk     Y772-p  -0.441 
##  7 EPHA2       vLEDDPEATyTTSGGkIPIR Y772-p  -0.441 
##  8 EPHA2       TyVDPHTYEDPNQAVLK    Y588-p  -0.547 
##  9 EPHA2       QSPEDVyFSK           Y575-p  -0.577 
## 10 EPHA2       qSPEDVyFSk           Y575-p  -0.577 
## 11 EPHA2       TYVDPHTyEDPNQAVLK    Y594-p  -0.590 
## 12 EPHA2       tYVDPHTyEDPNQAVLk    Y594-p  -0.590

Serine/Threonine

Each condition vs ctrl

data_diff_E_vs_ctrl_pST <- test_diff(pST_se_Set2, type="manual", test = "E_vs_ctrl")
## Tested contrasts: E_vs_ctrl
dep_E_vs_ctrl_pST <- add_rejections_SH(data_diff_E_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_E_vs_ctrl_pST, contrast = "E_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pST") 
Return_DEP_Hits_Plots(data = pST_Set2_form, dep_E_vs_ctrl_pST, comparison = "E_vs_ctrl_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## character(0)
data_diff_EC_vs_ctrl_pST <- test_diff(pST_se_Set2, type="manual", test = "EC_vs_ctrl")
## Tested contrasts: EC_vs_ctrl
dep_EC_vs_ctrl_pST <- add_rejections_SH(data_diff_EC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pST, contrast = "EC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pST") 
Return_DEP_Hits_Plots(data = pST_Set2_form, dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## character(0)
Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff", 
                               pw = "Epigenetic regulation of gene expression")
data_diff_EBC_vs_ctrl_pST <- test_diff(pST_se_Set2, type="manual", test = "EBC_vs_ctrl")
## Tested contrasts: EBC_vs_ctrl
dep_EBC_vs_ctrl_pST <- add_rejections_SH(data_diff_EBC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pST, contrast = "EBC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pST")
Return_DEP_Hits_Plots(data = pST_Set2_form, dep_EBC_vs_ctrl_pST, comparison = "EBC_vs_ctrl_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## [1] "Metabolism of RNA"

EC vs E

data_diff_EC_vs_E_pST <- test_diff(pST_se_Set2, type = "manual", 
                              test = c("EC_vs_E"))
## Tested contrasts: EC_vs_E
dep_EC_vs_E_pST <- add_rejections_SH(data_diff_EC_vs_E_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pST, contrast = "EC_vs_E",  add_names = TRUE, additional_title = "pST", proteins_of_interest = "EGFR")
Return_DEP_Hits_Plots(data = pST_Set2_form, dep_EC_vs_E_pST, comparison = "EC_vs_E_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## character(0)

## Note: Row-scaling applied for this heatmap

#data_results <- get_df_long(dep)

EBC vs EC

data_diff_EBC_vs_EC_pST <- test_diff(pST_se_Set2, type = "manual", 
                              test = c("EBC_vs_EC"))
## Tested contrasts: EBC_vs_EC
dep_EBC_vs_EC_pST <- add_rejections_SH(data_diff_EBC_vs_EC_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pST, contrast = "EBC_vs_EC",  add_names = TRUE, additional_title = "pST")
Return_DEP_Hits_Plots(data = pST_Set2_form, dep_EBC_vs_EC_pST, comparison = "EBC_vs_EC_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## [1] "Metabolism of RNA"
#data_results <- get_df_long(dep)

For Phosphosite Plus kinase enrichment

EC_vs_ctrl_pST_7AA <- left_join( (rowData(dep_EC_vs_ctrl_pST) %>% as_tibble() %>% 
  #filter(LTB4_vs_ctrl_p.adj< 0.05, LTB4_vs_ctrl_diff > 1.2) %>% 
    select(annotation = ID, Annotated_Sequence, HGNC_Symbol, fc = EC_vs_ctrl_diff,  p = EC_vs_ctrl_p.adj) %>% unique ),
  (all_pST_sites %>% select(Annotated_Sequence, Sequence_7_AA, HGNC_Symbol)),
  by=c("Annotated_Sequence", "HGNC_Symbol") ) %>% filter(!is.na(Sequence_7_AA)) %>% 
  mutate( peptide = str_to_upper(Sequence_7_AA) ) %>%
  select(annotation, peptide, fc, p) %>% as.data.frame()

Save

EC_vs_ctrl_pST_7AA %>% select(peptide, fc, p)  %>% write.table(file = "../Data/Kinase_enrichment/Batch2_Set2_EC_vs_ctrl_pST_7AA.txt", quote = FALSE, row.names = F, col.names = F, sep = "\t")

rowData(dep_EC_vs_ctrl_pY) %>% as_tibble() %>% 
  filter(EC_vs_ctrl_diff>1) %>%
  select(HGNC_Symbol ) %>% unique() %>%
  write.table("../Data/Kinase_enrichment/Batch2_Set2_EC_vs_ctrl_pY_FCmorethan1_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")

Session Info

sessionInfo()
## R version 4.2.3 (2023-03-15)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] lubridate_1.9.2             forcats_1.0.0              
##  [3] stringr_1.5.0               dplyr_1.1.2                
##  [5] purrr_1.0.2                 readr_2.1.4                
##  [7] tidyr_1.3.0                 tibble_3.2.1               
##  [9] ggplot2_3.4.2               tidyverse_2.0.0            
## [11] mdatools_0.14.0             SummarizedExperiment_1.28.0
## [13] GenomicRanges_1.50.2        GenomeInfoDb_1.34.9        
## [15] MatrixGenerics_1.10.0       matrixStats_1.0.0          
## [17] DEP_1.20.0                  org.Hs.eg.db_3.16.0        
## [19] AnnotationDbi_1.60.2        IRanges_2.32.0             
## [21] S4Vectors_0.36.2            Biobase_2.58.0             
## [23] BiocGenerics_0.44.0         fgsea_1.24.0               
## 
## loaded via a namespace (and not attached):
##   [1] circlize_0.4.15        fastmatch_1.1-4        plyr_1.8.8            
##   [4] igraph_1.5.1           gmm_1.8                lazyeval_0.2.2        
##   [7] shinydashboard_0.7.2   crosstalk_1.2.0        BiocParallel_1.32.6   
##  [10] digest_0.6.33          foreach_1.5.2          htmltools_0.5.6       
##  [13] fansi_1.0.4            magrittr_2.0.3         memoise_2.0.1         
##  [16] cluster_2.1.4          doParallel_1.0.17      tzdb_0.4.0            
##  [19] limma_3.54.2           ComplexHeatmap_2.14.0  Biostrings_2.66.0     
##  [22] imputeLCMD_2.1         sandwich_3.0-2         timechange_0.2.0      
##  [25] colorspace_2.1-0       blob_1.2.4             xfun_0.40             
##  [28] crayon_1.5.2           RCurl_1.98-1.12        jsonlite_1.8.7        
##  [31] impute_1.72.3          zoo_1.8-12             iterators_1.0.14      
##  [34] glue_1.6.2             hash_2.2.6.2           gtable_0.3.3          
##  [37] zlibbioc_1.44.0        XVector_0.38.0         GetoptLong_1.0.5      
##  [40] DelayedArray_0.24.0    shape_1.4.6            scales_1.2.1          
##  [43] pheatmap_1.0.12        vsn_3.66.0             mvtnorm_1.2-2         
##  [46] DBI_1.1.3              Rcpp_1.0.11            plotrix_3.8-2         
##  [49] mzR_2.32.0             viridisLite_0.4.2      xtable_1.8-4          
##  [52] clue_0.3-64            reactome.db_1.82.0     bit_4.0.5             
##  [55] preprocessCore_1.60.2  sqldf_0.4-11           MsCoreUtils_1.10.0    
##  [58] DT_0.28                htmlwidgets_1.6.2      httr_1.4.6            
##  [61] gplots_3.1.3           RColorBrewer_1.1-3     ellipsis_0.3.2        
##  [64] farver_2.1.1           pkgconfig_2.0.3        XML_3.99-0.14         
##  [67] sass_0.4.7             utf8_1.2.3             STRINGdb_2.10.1       
##  [70] labeling_0.4.2         tidyselect_1.2.0       rlang_1.1.1           
##  [73] later_1.3.1            munsell_0.5.0          tools_4.2.3           
##  [76] cachem_1.0.8           cli_3.6.1              gsubfn_0.7            
##  [79] generics_0.1.3         RSQLite_2.3.1          fdrtool_1.2.17        
##  [82] evaluate_0.21          fastmap_1.1.1          mzID_1.36.0           
##  [85] yaml_2.3.7             knitr_1.43             bit64_4.0.5           
##  [88] caTools_1.18.2         KEGGREST_1.38.0        ncdf4_1.21            
##  [91] mime_0.12              compiler_4.2.3         rstudioapi_0.15.0     
##  [94] plotly_4.10.2          png_0.1-8              affyio_1.68.0         
##  [97] stringi_1.7.12         bslib_0.5.0            highr_0.10            
## [100] MSnbase_2.24.2         lattice_0.21-8         ProtGenerics_1.30.0   
## [103] Matrix_1.6-0           tmvtnorm_1.5           vctrs_0.6.3           
## [106] pillar_1.9.0           norm_1.0-11.1          lifecycle_1.0.3       
## [109] BiocManager_1.30.22    jquerylib_0.1.4        MALDIquant_1.22.1     
## [112] GlobalOptions_0.1.2    data.table_1.14.8      cowplot_1.1.1         
## [115] bitops_1.0-7           httpuv_1.6.11          R6_2.5.1              
## [118] pcaMethods_1.90.0      affy_1.76.0            promises_1.2.1        
## [121] KernSmooth_2.23-22     codetools_0.2-19       MASS_7.3-60           
## [124] gtools_3.9.4           assertthat_0.2.1       chron_2.3-61          
## [127] proto_1.0.0            rjson_0.2.21           withr_2.5.0           
## [130] GenomeInfoDbData_1.2.9 parallel_4.2.3         hms_1.1.3             
## [133] grid_4.2.3             rmarkdown_2.23         shiny_1.7.4.1
knitr::knit_exit()